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β TIMESTAMPS
ο»Ώ00:16 Understanding Different Data Roles
01:48 Essential Data Skills and Tools
04:36 Building Projects to Showcase Skills
08:13 Creating a Portfolio for Your Projects
09:06 Optimizing LinkedIn and Resume
10:46 Applying for Jobs and Networking
12:38 Preparing for Interviews
14:25 Conclusion and Final Tips
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00:00:00
Avery: Here's how I would become
a data analyst if I had to
00:00:02
start all over again in 2025.
00:00:05
Now, I'm lazy and I'm impatient,
so this method that I'm going to
00:00:08
be choosing, the SPN method, is the
fastest and it's the lowest amount
00:00:12
of work to actually land a data job.
00:00:14
But it still is a lot of work.
00:00:16
Step one is I'd understand the different
data roles available in the data world.
00:00:20
There are so many different data
roles, and it's not just data analysts.
00:00:24
There are so many other roles, That
are just like data analysts, but
00:00:27
have slightly different names and
slightly different responsibilities.
00:00:30
For example, business intelligence
analyst, business intelligence
00:00:33
engineer, technical data analyst,
business analyst, healthcare
00:00:37
analyst, risk analyst, price analyst.
00:00:39
There are so many, literally
so many different options that
00:00:42
you could possibly choose from.
00:00:43
And they're all pretty similar for
the most part, but some things are
00:00:47
going to be slightly different.
00:00:48
So for example, a healthcare analyst,
you're going to be a data analyst.
00:00:51
But specializing and
looking at healthcare data.
00:00:53
Financial analysts, same thing.
00:00:55
You'd be looking at financial data.
00:00:57
A BI analyst, like a business
intelligence analyst, and a data
00:01:00
analyst, really a lot of the time are
going to be doing the exact same thing.
00:01:03
So it's important to be looking for
all these roles, understand what these
00:01:07
roles do and what their slight nuances
are, because there's a chance that
00:01:10
your previous experience is actually
valuable and would help you get a leg
00:01:15
up in applying for these different jobs.
00:01:16
So for example, If you have a business
degree and you're trying to transfer
00:01:19
into business analytics, becoming a
business analyst makes a lot of sense or
00:01:22
a financial analyst makes a lot of sense.
00:01:24
If you've worked previously as
a nurse or like a CNA, maybe
00:01:28
you become a healthcare analyst.
00:01:30
Whatever you've done previously,
there's probably a good chance
00:01:33
that that experience is valuable in
the data world to a specific role.
00:01:37
So even like I have a lot of
truck drivers in my business.
00:01:40
Bootcamp.
00:01:40
Those truck drivers can be logistics
analysts, they can be operations
00:01:43
analysts, they can be supply chain
analysts, because their previous
00:01:46
experience is actually valuable.
00:01:48
The second thing that I would do
is figure out what is actually
00:01:51
required, because here's the truth.
00:01:53
There is actually thousands
of data skills and tools.
00:01:58
and programming languages out there,
but if you try to master all of them,
00:02:02
you're going to be like 150 before you
feel prepared to start applying to jobs.
00:02:07
You're going to be dead.
00:02:08
It is impossible to learn.
00:02:10
It's impossible to master
all the different data tools
00:02:12
and skills and languages.
00:02:13
So by default, have to choose a few.
00:02:16
Now you have a decision to make
is which ones do you choose?
00:02:19
And I, like I said, I am lazy and I want
to do the least amount of work possible.
00:02:24
So I believe in the low hanging best.
00:02:26
Tasting fruit analogy.
00:02:28
If you can imagine that there's
a tree that has some sort of like
00:02:30
a peach or an apple on it, right?
00:02:31
The easiest fruit to grab is
always going to be the closest,
00:02:34
so it's the lowest hanging fruit.
00:02:36
But not only do you want the
lowest hanging fruit, you want
00:02:38
the tastiest fruit, right?
00:02:39
So this is stuff that is not only easy
to learn, but is extremely useful.
00:02:44
Those are the things you want to focus on.
00:02:45
Out of the thousands of data skills, those
are the ones you'll want to focus on.
00:02:49
You can do the research on your own,
if you'd like, by looking at job
00:02:51
descriptions and writing down what
is actually required, but that's a
00:02:55
lot of work and you can take it from
someone like me, who's been in this
00:02:57
space for about a decade now, looked at
literally thousands of job descriptions.
00:03:01
I even have my own data job board.
00:03:04
Findadatajob.
00:03:04
com.
00:03:05
And I look at it all the time
to see what is being required.
00:03:07
So I've done this research for you
already, and I will have a link to
00:03:11
my conclusions in the show notes
down below, but basically what you
00:03:14
need to know in terms of low hanging
fruit, it's Excel, Tableau, and SQL.
00:03:19
That is it.
00:03:20
Those are the top three skills that you
should be learning as a data analyst
00:03:23
when you're just trying to get started.
00:03:24
And if that is too hard to remember, you
can remember every turtle swims, right?
00:03:28
That's easy.
00:03:29
Excel.
00:03:30
Tableau and SQL.
00:03:31
That is where I'd start and I
wouldn't really veer off of that
00:03:34
until I've landed my first data job.
00:03:36
Now you might have noticed that I
didn't say Python and that might
00:03:38
come as a surprise to many of you
because you hear so much about
00:03:42
Python and how cool it is and how
popular it is and it is really cool.
00:03:45
It can do so many different things.
00:03:47
It's so powerful and it's actually my
favorite data tool but it's actually only
00:03:51
required on 30 percent of data analyst
roles and it's really hard to learn.
00:03:57
It takes a long time to learn
Python because Python is hard,
00:04:00
but also all programming is hard.
00:04:02
And if you don't have a programming
background, it's going to take a
00:04:04
long time to just kind of even get
your foot in the door in the Python
00:04:07
world and understand what's going on.
00:04:09
What's a variable?
00:04:10
What's a loop?
00:04:11
What's a function?
00:04:12
Those types of things just, they take
time and so if you only need it for
00:04:16
30 percent of the jobs, that means 70
percent of the jobs don't require it.
00:04:19
And once again, I am all about doing
the least amount of work possible
00:04:22
and doing it as quickly as possible.
00:04:23
So I say save Python for after your
first day at a job because it's really
00:04:27
just not needed to land that first one.
00:04:29
Once again, I have a free video that
kind of explains what skills you
00:04:32
should learn and in what order and why.
00:04:34
I'll have that in the
show notes down below.
00:04:36
The third thing that I would do if I was
trying to become a data analyst is try
00:04:40
to figure out how I'm going to convince
a hiring manager or recruiter to hire me,
00:04:44
even though I have no prior experience.
00:04:47
There's this thing called the cycle of
doom, which basically says I can't land a
00:04:51
data job because I don't have experience
because I can't land a data job.
00:04:55
And it's this never ending cycle
of, well, you're never going to get
00:04:57
a job unless you have experience.
00:04:59
You can never get experience
unless you get a job.
00:05:01
It's kind of like the
chicken or the egg, you know?
00:05:05
So you have to figure out, how am
I going to beat the cycle of doom?
00:05:08
And how am I going to convince
someone that, yeah, I am a data
00:05:11
analyst and you should hire me.
00:05:13
How would I do it, personally?
00:05:14
I'd build projects.
00:05:15
Projects are a great way that
you can demonstrate your skills.
00:05:19
It's basically the tangible evidence
for people to know that you can do
00:05:23
what your resume says you can do.
00:05:25
If you're unfamiliar with projects,
It's like almost doing pretend work
00:05:28
where you're pretending that you're
working for a certain company.
00:05:31
You take a data set and you analyze
it and publish your results.
00:05:35
We'll talk about where to publish them
here in a second, but basically it's
00:05:38
allowing you to learn with realistic data
with realistic problems, but also you're
00:05:42
creating some sort of evidence, like
literally physical evidence that you can
00:05:46
show to hiring managers, recruiters, and
be like, Hey, look, I can do these things.
00:05:50
I can be a data analyst.
00:05:51
I can use Excel.
00:05:52
I can use SQL.
00:05:53
I can create a data
visualization in Tableau.
00:05:55
Once I understand those three
things, the fourth thing that I would
00:05:58
personally do is start learning.
00:06:00
And I want to emphasize
this is not the first thing.
00:06:02
This is not the second thing.
00:06:02
This is not the third thing.
00:06:03
It's the fourth thing that I
would do is start learning.
00:06:06
And I would start learning Excel,
Tableau, SQL, every turtle swims, right?
00:06:10
And I would do that by building projects,
because I think building projects
00:06:14
is the most realistic way to learn.
00:06:16
I'll think it's It's the funnest
way to learn because just doing like
00:06:19
pointless exercises on like these
like interactive online learning
00:06:24
things, this is not realistic.
00:06:25
Like in real life, you're going
to be having real data sets.
00:06:27
You're not going to be in some
like controlled environment.
00:06:30
You're actually going to have to be
analyzing real data that's messy,
00:06:33
that has issues that has flaws
and you have to figure it out.
00:06:35
And so building projects is the
best way to learn because you're
00:06:38
also creating this tangible evidence
that you're going to be able to show
00:06:40
to hiring managers and recruiters.
00:06:42
You might be thinking, well,
where do I get started?
00:06:43
Well, you need to figure out
where you can find datasets.
00:06:46
You have to have a good dataset.
00:06:47
I just did an episode on this
recently, and I'll have the link
00:06:49
to the show notes down below.
00:06:50
But the simple answer, the
one word answer is Kaggle.
00:06:53
Kaggle is the best
place to find a dataset.
00:06:55
It's not the only place, and there's
other great resources, but if
00:06:57
you're only looking for one, Kaggle
is usually the place I would go.
00:07:00
And I'd personally build projects based
off of what you want to do ultimately.
00:07:04
So go back to step one and think about it.
00:07:06
Like if you have a business degree, let's
say you want to become a business analyst,
00:07:09
I would try to build projects that are
relevant to, to business analytics.
00:07:13
Maybe data on sales or marketing
or operations, anything
00:07:17
that's business related.
00:07:18
Those are the projects
I would try to seek out.
00:07:20
Or if you're not sure, like if you want
to be a business analyst or a healthcare
00:07:23
analyst, or maybe you don't even care.
00:07:25
You'll just take whatever you've got.
00:07:26
I would suggest doing projects
on lots of different industries.
00:07:29
Maybe dip into healthcare analytics.
00:07:31
Maybe do some people and HR analytics.
00:07:33
Maybe do a project on
manufacturing and engineering data.
00:07:37
That way you're getting exposed
to multiple different industries,
00:07:39
so you can kind of figure out
maybe what you're interested in.
00:07:42
You're creating a robust portfolio
that will be attractive to every
00:07:45
industry and multiple companies, right?
00:07:47
Because if you just focus on creating,
you know, business projects, but
00:07:50
let's say you want to become a
healthcare analyst, it's like, oh,
00:07:52
those projects don't really match up.
00:07:54
So.
00:07:54
That way you have a project for whatever
role you might be interested in.
00:07:57
So that's particularly
what I suggest doing.
00:07:59
And it's what we do inside of
my bootcamp, the Data Analytics
00:08:01
Accelerator is we learn Excel, SQL,
and Tableau by building projects.
00:08:06
And we built multiple projects
in different industries.
00:08:08
So that way we're very robust as can.
00:08:10
The fifth thing I would do if I was
trying to become a data analyst.
00:08:13
is create a home for my projects.
00:08:15
And this is actually
what's called a portfolio.
00:08:17
You know, projects are something that
we do but if you just do them and you
00:08:20
don't publish them and you don't share
them, they don't actually do much good.
00:08:23
You need to create a portfolio
to home these projects.
00:08:26
And the portfolio platform you'll
hear the most about is GitHub.
00:08:29
And I have a controversial
take that I'm not a fan of it.
00:08:31
I don't think GitHub is
meant to be a portfolio.
00:08:34
Now that's me being a little bit picky,
but I just don't think it's the best
00:08:38
option if you're choosing from scratch.
00:08:40
What you need to do is make sure
that your readmes are really good,
00:08:42
because if you have a good readme
on your GitHub, then it can work.
00:08:45
But if you're starting from scratch,
I recommend doing something like
00:08:48
LinkedIn, using the featured section.
00:08:50
Or choose GitHub Pages, which is from
GitHub, but kind of a separate product,
00:08:54
and it's their portfolio solution.
00:08:55
It's actually what GitHub
recommends as a portfolio.
00:08:58
Or I really like Card, C A R R D.
00:09:00
It's just a simple website builder,
be really great options inside the
00:09:04
accelerator, my bootcamp, so any of
those three would work just fine.
00:09:06
The sixth thing I would do is make
sure that my LinkedIn and resume
00:09:10
are up to date and optimized.
00:09:12
And I would do this early, even
before I've actually mastered Excel
00:09:16
or I've, you know, tackled Tableau.
00:09:18
The earlier you do this,
the better, because.
00:09:19
Your LinkedIn is your professional
business card to the world.
00:09:23
One of the really cool things is LinkedIn
has a feature called Open to Work.
00:09:26
There's two different settings on it.
00:09:27
We can talk about it later, but
basically you can have Open to Work
00:09:31
for the entire world or you can just
have Open to Work for recruiters.
00:09:33
And either way, if you set up
your LinkedIn correctly, your
00:09:36
LinkedIn can start to work for you.
00:09:38
And instead of you going out and
applying for jobs, recruiters
00:09:40
and hiring managers are actually
applying to you for specific jobs.
00:09:43
They'll reach out to you and be like, Hey,
I think you're a good fit for this job.
00:09:46
So having an optimized
LinkedIn is, is really key.
00:09:48
And then of course, having an
optimized resume is a must because
00:09:52
once you start applying for jobs.
00:09:53
If your resume isn't optimized, you're
probably not going to get many interviews.
00:09:57
And the reason is there's so many
candidates trying to get into data
00:10:01
analytics roles, especially the
entry level ones, that recruiters
00:10:04
and hiring managers have to use
what's called the ATS, which is
00:10:07
the Applicant Tracking System.
00:10:08
And basically it's, it's computer, it's
AI, it's It's actually not even really
00:10:13
that complicated, but there are certain
things you need to do on your resume to
00:10:16
have it be optimized and ATS friendly, so
you can get past the computer screening
00:10:20
and actually have a human being look at
your resume, because it's so frustrating
00:10:24
when you get rejection after rejection
after rejection that you don't even know
00:10:27
if a human's looking at your resume.
00:10:29
A lot of the times you're just getting
rejected by the ATS, and so you need to
00:10:32
make sure you have an optimized resume.
00:10:34
So, in terms of having an optimized
resume, it would basically look like
00:10:36
not having any columns on your resume,
or any tables on your resume, and
00:10:40
then using really key words that match
the job descriptions, so that way you
00:10:43
appear as a good applicant to the ATS.
00:10:46
The seventh step that I would take
is to start applying, and I think
00:10:50
this is obvious, but a lot of people
don't ever start applying for jobs.
00:10:54
And I get it, because it's scary.
00:10:56
How do you know if you're
ready to land a data job?
00:10:58
It's hard to know, and you probably
will never feel ready, so I suggest
00:11:02
just start applying anyways.
00:11:03
And when you start applying,
don't only apply on LinkedIn jobs.
00:11:07
LinkedIn jobs is where everyone applies,
and there's going to be hundreds of
00:11:10
candidates in a matter of a few days
on those platforms, the majority of the
00:11:14
time, because everyone's doing that.
00:11:16
So you might want to try something
new, like going to company websites or
00:11:20
checking out my job board, findadatajob.
00:11:22
com or some other combination
of other job websites.
00:11:25
The point here is you need to
be looking at multiple places
00:11:28
and actually start applying.
00:11:30
I know it's scary, but just do it scared.
00:11:32
The next step I would do in this process
is I would really try to be networking.
00:11:36
And I, I would try to be networking
the entire time, like even in step one.
00:11:39
But this is where I fit on
today's roadmap is step eight.
00:11:42
So it's way easier to get
hired when you know someone.
00:11:46
In fact, my brother was just recently
looking for a job and having a
00:11:49
hard time and he ended up Getting
an interview and landing that job
00:11:52
because his wife's friend works there.
00:11:55
And like, I can't tell you how
often that actually happens.
00:11:58
So networking doesn't have to be hard.
00:12:00
You can do it on LinkedIn by
posting and commenting on LinkedIn.
00:12:02
I think that's really important to do, but
I understand that's hard and a scary step.
00:12:07
One thing that's really a lot easier is
just to talk to your friends and family.
00:12:10
Just say, Hey, I'm trying
to become a data analyst.
00:12:12
Do you know anyone who's a data analyst?
00:12:14
Does your company hire data
analysts and have a conversation?
00:12:17
You're not even really
asking them anything.
00:12:19
You're just opening a conversation.
00:12:20
I know this is hard and I know it's
uncomfortable and I know it's not fun.
00:12:24
Like it's much more fun to learn data
skills than it is to network, but
00:12:27
honestly, networking gets you the same,
if not better results than upscaling
00:12:31
and actually learning new data things.
00:12:33
So you can't be ignoring this.
00:12:34
Couldn't be ignoring this.
00:12:35
I have to be networking,
no matter how hard it is.
00:12:38
Now, if all is going well, and I'm doing
all the previous eight things that I've
00:12:41
talked about, I think at this point
I'd probably start to land interviews.
00:12:45
There's two parts to an interview,
the technical and the behavioral.
00:12:48
The technical interview is when
they're going to be asking you
00:12:50
questions about data skills.
00:12:52
It might be like, Excel questions or data
visualization questions or oftentimes
00:12:56
sequel questions and I'll ask you to
write certain sequel queries This can
00:13:00
be really scary and intimidating and
honestly, they can be really hard The
00:13:04
cool part is they don't always occur
or or if they occur they occur very
00:13:08
easily Sometimes they're very hard.
00:13:10
Sometimes they're very easy.
00:13:10
It really just depends and to prepare
for the technical resources There's
00:13:14
a lot of things that I could do.
00:13:15
There's a lot of resources out
there that would help me prepare.
00:13:18
Um, there's something called Scrata
Scratch that I'll have a link in the show
00:13:21
notes down below that you guys can check.
00:13:22
There's Data Lemur.
00:13:23
There's a bunch of tools that
will help you prepare for
00:13:25
these technical interviews.
00:13:27
Behavioral interview is going to be
more like them trying to feel for
00:13:30
who you are and what you've done
previously and like how you would
00:13:33
act as a human being, as an employee.
00:13:35
And that is a little bit harder to
prepare for because it's more of like,
00:13:39
instead of answering technical questions,
it's answering like personal questions.
00:13:43
There's not a whole lot
of resources out there.
00:13:45
One of the things you would want
to do is use the STAR method.
00:13:48
You want to answer every question by
saying, this is the situation I was
00:13:51
in, this is the task I was given, this
was the action I took, and this is the
00:13:55
results that came from that action.
00:13:57
And if you answer using that method,
most of the time you'll be good.
00:14:00
It can be scary, and there's not a whole
lot of resources out there for this.
00:14:03
So do you want to check
out one that I made?
00:14:04
It's called interview simulator.
00:14:06
io, and it basically helps you
practice these questions where
00:14:10
I'll ask you the question via video
and you will respond via video.
00:14:14
And then we'll actually grade your
answer and tell you what you did
00:14:17
well and where you could improve.
00:14:18
It's a pretty cool software.
00:14:20
I'll link for that in the
show notes down below as well.
00:14:22
Wow, lots of links in the show
notes, so be sure to check those out.
00:14:25
So those are the nine steps that I
would take if I had to start from
00:14:29
scratch and land a day job in 2025.
00:14:31
And remember, I'm lazy, I'm trying
to do this the easiest way possible.
00:14:35
This is This is what
I call the SPN method.
00:14:36
You need to learn the right skills, not
all the skills, but the right skills.
00:14:39
You need to build projects
and put them on a portfolio.
00:14:41
That's the P part.
00:14:42
And then you need to be
networking, updating your
00:14:44
LinkedIn and updating your resume.
00:14:46
That's the N part.
00:14:47
And it's the easiest
way to land a data job.
00:14:50
Now you can do all this stuff that
I told you on your own and you'd
00:14:53
be 100 percent okay, but it's a lot
more fun to do it in community and
00:14:56
it's a lot easier to do with a coach.
00:14:58
Once again, I'm all about doing it
fast, And it's much easier to do
00:15:02
that with a given curriculum where
you don't have to be questioning.
00:15:05
Am I doing this right?
00:15:06
How do I actually do this?
00:15:07
So on and so forth.
00:15:08
And so that's why I created the data
analytics accelerator program, which
00:15:11
is basically a 10 week bootcamp to
help you land your first day at a job.
00:15:14
We'll go over all of these nine steps.
00:15:16
Hand by hand, step by step together, and
make sure you're ready to land a data job.
00:15:20
If you want to check that out,
you can go to datacareerjumpstart.
00:15:23
com slash D A A D A A standing
for Data Analytics Accelerator.
00:15:27
And of course, I'll have a link to
that in the show notes down below.
00:15:30
Let me know what I missed
and what questions you have.
00:15:32
I'll try to respond to everyone in
the comments down below if you're
00:15:34
watching on YouTube or on Spotify.
00:15:36
And I wish you the best of luck in 2025.
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